Outcomes in patients with cirrhosis evaluated through routine healthcare data

Background: Mortality from liver disease is rising. Recent efforts have been focussed on early detection and prevention of disease progression. Transient elastography (TE) is commonly used in clinical practice to diagnose fibrosis and is an accurate predictor of mortality, decompensation, and hepatocellular carcinoma (HCC). Electronic health records (EHR) have been used to observe ‘real world’ data in large cohorts of patients with liver disease. Method: EHR data was used to observe outcomes in patients with advanced fibrosis. This included a systematic review followed by synthesis and validation of a code set to identify cirrhosis and complications in EHR data. Data extracted from EHR was used to assess survival and competing risk of liver and non-liver related events in a cohort of patients with advanced chronic liver disease defined by TE. Data was analysed to examine patterns of screening and surveillance for complications of cirrhosis in accordance with current practice guidelines. Results: The developed consensus code set showed improved performance characteristics for identifying cirrhosis in comparison to previously used codes. In the analysis of over 3000 patients with advanced fibrosis, liver stiffness was associated with the development of varices, the transition from compensated cirrhosis to decompensated cirrhosis, and the development of hepatocellular carcinoma. Only a minority of patients had undergone the recommended surveillance interventions. Conclusion: Electronic health databases can be used to evaluate screening and surveillance strategies, and to accurately define clinical progression and outcomes in large patient cohorts and estimates of disease progression in EHR cohorts are comparable to landmark studies of patients diagnosed using liver vi biopsy. Transient elastography is strongly associated with outcomes in patients with advanced fibrosis. This study highlights the utility of EHR data in contemporary research practice in cirrhosis, highlighting the potential for a registry to impact on patient care and to improve outcomes for patients with cirrhosis

Atrial fibrillation and frailty: An observational cohort study using electronic healthcare records

Atrial fibrillation is common in older people, and is associated with increased mortality and stroke. Patients with atrial fibrillation/flutter (AF) also commonly have frailty, which is associated with increased risk of a range of further adverse clinical outcomes. However, there is a lack of evidence on the burden and management of AF in people with frailty. A study using the primary care electronic health records of 536,955 patients aged ≥65 years was conducted to investigate the burden of frailty and AF amongst older people, and their associations with clinical outcomes. A systematic review and meta-analysis was completed to establish the current knowledge base, and to inform the quantitative analyses. Baseline characteristics were described and compared between those with and without AF as well as by frailty category according to the electronic frailty index. Rates of all-cause mortality, stroke, bleeding (intracranial and gastrointestinal), transient ischaemic attack (TIA), and falls were calculated per 1000 person-years, and compared with the non-AF patient population. Cox proportional hazards modelling was used to determine unadjusted and adjusted risk for each clinical outcome and mortality, and presented as hazard ratios (HR) alongside 95% confidence intervals. The association between oral anticoagulation (OAC) prescription stratified by frailty category with clinical outcomes was investigated using Cox proportional hazards modelling. At baseline, 61,177 (11.4%) patients had AF. People with AF had a higher burden of frailty than those without (89.5% vs. 55.3%) and had higher rates of mortality, stroke, TIA and bleeding. Of patients with AF and eligible for OAC, it was prescribed in 53.1% (41.7% in robust, mild frailty 53.2%, moderate 55.6%, severe 53.4%). OAC was associated with a 19% reduction in all-cause mortality (HR 0.81, 95%CI 0.77-0.85) and 22% reduction in stroke (HR 0.78, 0.67-0.92). There was no statistically significant difference in rates of bleeding between those prescribed and not prescribed OAC. For the first time in a large representative cohort of older people, this study quantified the burden of AF and frailty, and their association with a range of clinical outcomes. This study found no evidence that OAC should be withheld on the basis of concomitant frailty

Towards mitigating health inequity via machine learning: a nationwide cohort study to develop and validate ethnicity-specific models for prediction of cardiovascular disease risk in COVID-19 patients

Background Emerging data-driven technologies in healthcare, such as risk prediction models, hold great promise but also pose challenges regarding potential bias and exacerbation of existing health inequalities, which have been observed across diseases such as cardiovascular disease (CVD) and COVID-19. This study addresses the impact of ethnicity in risk prediction modelling for cardiovascular events following SARS-CoV-2 infection and explores the potential of ethnicity-specific models to mitigate disparities. Methods This retrospective cohort study utilises six linked datasets accessed through National Health Service (NHS) England’s Secure Data Environment (SDE) service for England, via the BHF Data Science Centre’s CVD-COVID-UK/COVID-IMPACT Consortium. Inclusion criteria were established, and demographic information, risk factors, and ethnicity categories were defined. Four feature selection methods (LASSO, Random Forest, XGBoost, QRISK) were employed and ethnicity-specific prediction models were trained and tested using logistic regression. Discrimination (AUROC) and calibration performance were assessed for different populations and ethnicity groups. Findings Several differences were observed in the models trained on the whole study cohort vs ethnicity-specific groups. At the feature selection stage, ethnicity-specific models yielded different selected features. AUROC discrimination measures showed consistent performance across most ethnicity groups, with QRISK-based models performing relatively poorly. Calibration performance exhibited variation across ethnicity groups and age categories. Ethnicity-specific models demonstrated the potential to enhance calibration performance for certain ethnic groups. Interpretation This research highlights the importance of considering ethnicity in risk prediction modelling to ensure equitable healthcare outcomes. Differences in selected features and asymmetric calibration across ethnicities underscore the necessity of tailored approaches. Ethnicity-specific models offer a pathway to addressing disparities and improving model performance. The study emphasises the role of data-driven technologies in either alleviating or exacerbating existing health inequalities. Evidence before this study Research has suggested that SARS-CoV-2 infections may have prognostic value in predicting later cardiovascular disease outcomes, two diseases where ethnicity-based health inequalities have been observed. Existing health inequalities are at risk of being exacerbated by bias in emerging data-driven technologies such as risk prediction models, and there currently exists no recommended practice to mitigate this issue. Model performances are not typically stratified by ethnic groups and, if reported, ethnic groups are often only included in higher-level categories that have been criticised for simplicity of definition and for missing key ethnic heterogeneity